ML Community Urged to Develop AI-Augmented Peer Review System to Address Submission Crisis
A position paper argues that machine learning conferences face a peer review crisis due to exponential growth in manuscript submissions outpacing reviewer capacity. The authors propose using AI assistants to support—not replace—human reviewers, authors, and decision-makers across the review process. The proposal addresses concerns about review quality and scalability while maintaining scientific integrity standards.
Peer review in machine learning is experiencing strain from rapidly increasing manuscript submissions to major venues like NeurIPS, ICML, and ICLR, creating concerns about review quality, consistency, and reviewer fatigue. The position paper advocates for developing an AI-augmented peer review ecosystem where Large Language Models serve as collaborative tools rather than replacements for human judgment. Proposed AI roles include factual verification, reviewer performance guidance, author assistance for quality improvement, and support for Area Chairs in decision-making. The authors emphasize that building such systems requires access to more granular, structured, and ethically-sourced peer review data. They outline a research agenda with experiments to develop and validate these AI assistants while addressing technical and ethical challenges. The paper calls on the ML community to proactively build this infrastructure to ensure continued scientific validation integrity and scalability.
What's missing
The paper does not discuss potential risks of AI bias in peer review, specific metrics for measuring review quality improvement, or how different research communities outside ML might adapt similar systems. Additionally, the paper does not address whether access to peer review data raises privacy concerns for reviewers or authors, or provide concrete timelines for implementation.
What different sources said
- arXiv cs.AICenter
Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem
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